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This work presents the recently developed Adaptive Fourier Decomposition (AFD) approach-based ECG compression technique that ensures considerable data reduction at guaranteed reconstructed signal quality. Unlike existing decomposition techniques with fixed Basis functions, AFD selects distinct Basis function in accordance with the input signal resulting in fast energy convergence with good signal fidelity. However, the complex nature of AFD coefficients and Basis functions restrain the compression efficiency. In the proposed technique AFD algorithm is modified to obtain integer valued Basis functions causing considerable improvements in the Compression Ratio (CR) and reconstructed signal quality. Additionally, the complex-valued AFD coefficients are encoded with the lossless Adaptive Bit Length Encoding (ABLE) approach which further modify the compression efficiency. The comprehensive experimentation of the proposed technique was done on various standard and self-recorded databases on varying values of decomposition levels (N) and window size (W). The proficiency of the technique was further analysed by computing optimum N in accordance with the pre-defined distortion to achieve maximum CR. Average Percentage Root Mean Square difference (PRD%), Weighted Wavelet PRD (WWPRD), Wavelet Energy based Diagnostic Distortion (WEDD), Peak Signal to noise Ratio (PSNR), CR and Quality Score (QS) obtained are 0.68, 18.24, 6.86, 47.19, 22.11 and 37.24 respectively when measured on 48 records of MIT-BIH arrhythmia database of 5 min duration at N = 50 and W = 2000. The experimental results demonstrated the competency of the proposed technique with excellent CR at negligible reconstruction error as compared to the other state of the art techniques. [ABSTRACT FROM AUTHOR]

Over the past few years, the use of chaotic maps have surfaced to become an important topic in the field of image encryption. The majority of chaos-based algorithms employed single or multiple rounds of confusion and diffusion. However, such algorithms increase the security and computational cost of the encryption process. On the other hand,a higher computational process is required to maintain security. To overcome this issue, a robust image encryption method using the features of two dimensional coupling map (2D-CM) and Discret Wavelet Transform(DWT) is proposed. Additionally, low-frequency component of DWT used for encryption can significantly save storage space thereby reducing computational overheads without compromising the security of the encryption. Finally, the cover image is combined with higher frequency coefficients to obtain the whole image, which will be totally secure against external attacks. The proposed encryption technique undergoes various security analyses, including Information Entropy (I en) , Correlation Coefficients (CC), Number of Pixel Change Rate (NPCR), Unified Average Change Intensity (UACI) and computational complexity and histogram analysis. The NPCR value and UACI obtained for all the images under simulation are greater than 99% and 33, respectively. Furthermore, results show that the proposed method has low correlation, high key sensitivity and sufficient key space (2 256) to withstand against external attacks. [ABSTRACT FROM AUTHOR]

Generally, spatial domain and transform domain steganography techniques are used to achieve the covert communication. The main drawback of embedding in frequency domain is rounding error appeared in the embedded secret message. Information is lost when integer pixel values are restored from the frequency domain, causing the embedded message to be distorted. A minor distortion of the embedded message renders extraction impossible and results in loss of embedded secret message. In the present study, a novel imperceptible Discrete Cosine Transform (DCT) and chaotic map based steganographic algorithm for secure communication has been proposed. The proposed method performs an adaptive n-Ary secret data embedding in the higher frequency DCT coefficients and removes the distortion. Additionally, the security of secret data is enhanced using chaotic maps to select the random DCT coefficients for embedding. The experimental results confirm that the proposed technique has high embedding capacity (EC), excellent stego-image quality and capability to withstand against malicious users. To examine security of the proposed method, the key space and key sensitivity parameter were used. Maximum EC = 425,780 bits was achieved with Peak Signal to Noise Ratio (PSNR) = 32.2218, 29.8804, 32.2507, 33.3327, 32.3963 and 31.4936 dB in case of Lena, Baboon, Jetplane, Elaine, Pepper and Boat respectively. The evaluated results show that the proposed method outperforms the existing steganography techniques in terms of EC and PSNR. Additionally, quality of stego image is identified by universal image quality index (Q), Bit Error Rate (BER) and Structural Similarity Index (SSIM). [ABSTRACT FROM AUTHOR]

In the recent years chaotic map and Discrete Cosine transform (DCT) have surfaced to become an important field in data hiding methods. In this proposed work, a randomly high embedding chaotic value differencing method (RCVD) based on combined application of DCT and Chaotic map is proposed. DCT has energy compaction property which removes the rounding error problems in the embedding and extraction process. Whereas, chaotic maps have significant properties of sensitivity to initial and control parameters, which help to provide security to the proposed algorithm. The embedding of secret data is done by hybrid method namely randomly chaotic value differencing (RCVD). RCVD maintains the human vision system (HVS) by embedding more data in smooth area than in edge area. The experimental results are calculated in terms of Peak Signal to Noise Ratio (PSNR) and embedding capacity. The security of the proposed method is examined by the key space, key sensitivity parameter and histogram analysis. Additionally universal image quality index (Q) and entropy are also calculated for the proposed method. The evaluated results show that the proposed method outperforms the existing steganography techniques in terms of embedding capacity and PSNR. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) is essentially a significant physiological signal required in the diagnosis of cardiac disorders. For remote healthcare assistance, ECG signal along with patient's meta-data is communicated over the public network. During communication, security and privacy of patient's sensitive information is a major issue. Presently, a common steganography technique is being applied on the entire ECG signal. Since ECG signal consists of clinically more significant QRS regions as well as less significant non-QRS regions and employing same steganography approach on both the regions is not admissible. In this work, a hybrid approach is proposed for concealing the sensitive information in 2-dimensional (2D) ECG. A fusion of integer wavelet transform and modified least significant bit (IWT-mLSB) approach is applied in the pivotal QRS complex region; while pixel inverted pixel value differencing (PI-PVD) technique is implemented in the non-QRS region to hide the confidential data. The performance of the proposed algorithm is evaluated on standard as well as self-recorded database in terms of statistical parameters, clinically critical metrics, heart rate variability (HRV) analysis, embedding capacity (EC) and bit error rate (BER). The security of the proposed algorithm is further evaluated in terms of key space and key sensitivity. A comparative analysis with other state-of-the-art techniques exhibits the competency of the proposed technique. [ABSTRACT FROM AUTHOR]

In this work, a novel hybrid algorithm has been developed for achieving imperceptible and robust image steganography for secure data communication. The novelty of the work lies in the careful manipulation of higher frequency coefficient of Discrete Cosine Transform (DCT) to maintain the perceptual quality of the image followed by embedding secret bits in the controlled DCT coefficients using random locations identified by deterministic Coupled Chaotic Map (CCM). The randomness of the CCM map is confirmed by National Institute of Standards and Technology, DIEHARD, ENT and TestU01 test suites. The experimental results demonstrate that the proposed technique has excellent stego-image quality keeping zero Bit Error Rate at maximum embedding capacity (EC). The proposed method has capability to withstand against malicious users as well as outperforms existing steganography techniques in terms of EC, and Peak Signal to Noise Ratio. The security of the proposed technique is subsequently examined by the key length, key sensitivity parameter and histogram analysis. [ABSTRACT FROM AUTHOR]

The present paper proposes a complexity sorting and coupled chaotic map mutation mechanism for compression-then-encryption of the Electrocardiogram (ECG) signals. The compressed-then-encrypted ECG is wirelessly transmitted using orthogonal frequency division multiplexing scheme modified to perform impair sample correction. The compression based on complexity sorting involves following steps: Beat Detection, 2D ECG array formation, Period Normalization, Dc Equalization, Complexity Sorting, Codec Quantization and JPEG2000 codec. The 2D compression results in reduced memory requirements for the clinical data storage. The coupled chaotic based on mutation mechanism for ECG encryption randomizes the ECG array to prevent the attackers from deducing the confidential information from it. The compressed-then-encrypted ECG bitstream is transmitted through the Rayleigh fading wireless channel. At the receiver end, the erroneous sample is corrected by moving median filtering (MMF) mechanism to reduce Percentage Root mean square Difference (PRD). The 2D compressed and the encrypted ECG remains diagnosable after reconstruction. The average compressor metrics Compression Ratio (CR), PRD, and Quality Score (QS) were 72.81 ± 15.90, 2.57 ± 1.68%, and 44.36 ± 29.92 respectively on MIT-BIH Arrhythmia database. Furthermore, 2D compressed ECG is effectually encrypted as validated by the histogram analysis, Information Entropy(En), Entropy Score (ES), and the correlation coefficient analysis. The En, ES, and the correlation values were equal to 7.996, 0.999, and 0.0042 respectively for an 8-bit quantization resolution. Moreover, due to MMF approach at the particular Channel Signal to Noise Ratio (SNRc = 15 dB) & BER = 10−2, the PRD reduces from 45% (without erroneous sample correction) to 2.2% (with erroneous ECG sample correction). [ABSTRACT FROM AUTHOR]

In e-healthcare paradigm, the physiological signals along with patient's personal information need to be transmitted to remote healthcare centres. Before sharing this sensitive information over the unsecured channel, it is prerequisite to protect it from unauthorised access. The proposed method explores ECG signal as the cover signal to hide patient's personal information without disturbing its diagnostic features. Chaotic maps are used to randomly select the embedding locations in the non-QRS region while excluding the sensitive QRS region of ECG train. Optimum Location Selection algorithm has been designed to select the embedding locations in non-QRS embedding region. The proposed algorithm is thoroughly examined and the distortion is measured in terms of statistical parameters and clinical measures such as PRD, PRDN, PRD1024, PSNR, SNR, MSE, MAE, KL-Divergence, WWPRD and WEDD. The robustness of the algorithm is verified using the parameters such as key space and key sensitivity. The implementation has been extensively tested on all the 48 records of the standard MIT-BIH Arrhythmia database, abnormal databases [CU-VT, BIDMC-CHF and PTB (leads I, II and III)] and self-recorded data of 20 subjects. The algorithm yields average PRD, MSE, KL-Divergence, PSNR, WWPRD and WEDD of 4.7 × 10−3, 1.13 × 10−5, 1.29 × 10−5, 50.28, 0.15 and 0.04 at an average maximum EC of 0.45(96876 bits) on MIT-BIH Arrhythmia database and 0.016, 3.38 × 10−5, 1.8 × 10−4, 46.03, 0.13 and 0.03 respectively at an average maximum EC of 0.47 (102571 bits) on self-recorded data which clearly reveals the competency of the proposed algorithm in comparison with the other state of the art ECG steganography approaches. [ABSTRACT FROM AUTHOR]

In this paper, a joint use of the discrete cosine transform (DCT), and differential pulse code modulation (DPCM) based quantization is presented for predefined quality controlled electrocardiogram (ECG) data compression. The formulated approach exploits the energy compaction property in transformed domain. The DPCM quantization has been applied to zero-sequence grouped DCT coefficients that were optimally thresholded via Regula-Falsi method. The generated sequence is encoded using Huffman coding. This encoded series is further converted to a valid ASCII code using the standard codebook for transmission purpose. Such a coded series possesses inherent encryption capability. The proposed technique is validated on all 48 records of standard MIT-BIH database using different measures for compression and encryption. The acquisition time has been taken in accordance to that existed in literature for the fair comparison with contemporary state-of-art approaches. The chosen measures are (1) compression ratio (CR), (2) percent root mean square difference (PRD), (3) percent root mean square difference without base (PRD1), (4) percent root mean square difference normalized (PRDN), (5) root mean square (RMS) error, (6) signal to noise ratio (SNR), (7) quality score (QS), (8) entropy, (9) Entropy score (ES) and (10) correlation coefficient ( r ). Prominently the average values of CR, PRD and QS were equal to 18.03, 1.06, and 17.57 respectively. Similarly, the mean encryption metrics i.e. entropy, ES and r were 7.9692, 0.9962 and 0.0113 respectively. The novelty in combining the approaches is well justified by the values of these metrics that are significantly higher than the comparison counterparts. [ABSTRACT FROM AUTHOR]

Heart rate variability (HRV) is the output of multiple physiological control mechanisms that occurs over a wide range of complex time scales. The multiscale entropy (MSE) of RR interval time series is a well established complexity based nonlinear technique, to analyze HRV at different time-scales using sample entropy (SampEn). Determination of SampEn requires a priori determination of two parameters; pattern length ( m) to be compared and tolerance threshold value ( r) to accept the similarity between the patterns. This study aims to investigate the applicability of previously recommended value of r to be 0.1-0.2 times the standard deviation of time series for MSE analysis. MSE at higher scales are found to be inappropriate with recommended range of r. Therefore to find maximum MSE at higher time scales, calculation of MSE with different possible values of r need to considered. But this approach is very time consuming and arduous. Further, MSE is found to be decreased significantly in function of time scales. [ABSTRACT FROM AUTHOR]